Atrapos：元路径查询工作负载的实时评估
Atrapos: Real-time Evaluation of Metapath Query Workloads
AI总结：
本文提出ATRAPOS方法，通过稀疏矩阵乘法与基于Overlap Tree的中间结果缓存相结合，实时检测频繁子元路径以复用计算结果，从而加速异构信息网络上的元路径查询工作负载评估。
摘要：
异构信息网络（HINs）表示不同类型的实体及其之间的关系。在此类网络中探索、分析和提取知识依赖于元路径查询，这些查询识别由不同语义关系连接的实体对。虽然在大规模、网络规模的HIN上实时评估元路径查询工作负载在计算成本方面要求极高，但当前方法并未利用查询之间的相互关系。在本文中，我们提出了ATRAPOS，这是一种用于实时评估元路径查询工作负载的新方法，它结合了高效稀疏矩阵乘法与中间结果缓存。ATRAPOS通过实时检测工作负载查询中频繁出现的子元路径，使用一种定制数据结构——Overlap Tree，以及相关的缓存策略，来选择要缓存和复用的中间结果。我们在真实数据上的实验研究表明，ATRAPOS加速了HIN上的探索性数据分析和挖掘，在所有考察的场景中均优于现成的缓存方法和最先进的研究原型。
英文摘要：
Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring, analysing, and extracting knowledge from such networks relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present ATRAPOS, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. ATRAPOS selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that ATRAPOS accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios. -- Note that this version of our work is more extended than the one presented in TheWebConf 2023 (doi: 10.1145/3543507.3583322)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
